In recent weeks, Facebook and YouTube have strained to explain why they won't ban Alex Jones' Infowars, which has used its verified accounts to spread false news and dangerous conspiracy theories on the platforms. Meanwhile, the midterms are approaching, and Facebook won't say definitively whether the company has found any efforts by foreign actors to disrupt the elections. Facebook did recently say that it will start to remove misinformation if it may lead to violence, a response to worrisome trends in Myanmar, India, other countries. The social media platforms are being called on to explain how they deal with information that is wrong--a question made even more complicated because the problem takes so many forms. To understand the many forms of misinformation and disinformation on social media, we recently spoke with Claire Wardle, the executive director of First Draft, a nonprofit news-literacy and fact-checking outfit based at Harvard University's Kennedy School, for Slate's tech podcast If Then. We discussed how fake news spreads on different platforms, where it's coming from, and how journalists might think--or rethink--their role in covering it. The interview has been edited and condensed for clarity. Listen to If Then by clicking the arrow on the audio player below, or get the show via Apple Podcasts, Overcast, Spotify, Stitcher, or Google Play.

Banco Bradesco is seeing returns on the investments it has made in artificial intelligence, with employees and clients of the bank using the service 33 million times in the first half, it reported on Thursday. "Bradesco Inteligência Artificial" is based on IBM's Watson and responds to questions about products and services using natural language capabilities. It is not clear what growth rate this represents, as Bradesco has not reported results from its AI products previously. Bradesco spent BRL6 billion ($1.6 billion) on IT investments last year. As well as the AI capabilities, that money also went to improved online services, research into blockchain technology, and the bank's open innovation program which involves innovation labs and partnerships with startups.

One of the oldest video calling apps is still one of the best, but both people need to be using Skype for calls to be free. Otherwise, you need to pay to call a landline or mobile number. Some people still use their phones to talk, not just text and surf. If you're one, you can also cut costs when dialing. So long as you're on Wi-Fi -- whether it's your existing wireless network at home or a free Wi-Fi hotspot -- you can take advantage of apps that let you make free "VoIP" calls (Voice over Internet Protocol) on your smartphone or tablet.

Interactive reinforcement learning (IRL) extends traditional reinforcement learning (RL) by allowing an agent to interact with parent-like trainers during a task. In this paper, we present an IRL approach using dynamic audio-visual input in terms of vocal commands and hand gestures as feedback. Our architecture integrates multi-modal information to provide robust commands from multiple sensory cues along with a confidence value indicating the trustworthiness of the feedback. The integration process also considers the case in which the two modalities convey incongruent information. Additionally, we modulate the influence of sensory-driven feedback in the IRL task using goal-oriented knowledge in terms of contextual affordances. We implement a neural network architecture to predict the effect of performed actions with different objects to avoid failed-states, i.e., states from which it is not possible to accomplish the task. In our experimental setup, we explore the interplay of multimodal feedback and task-specific affordances in a robot cleaning scenario. We compare the learning performance of the agent under four different conditions: traditional RL, multi-modal IRL, and each of these two setups with the use of contextual affordances. Our experiments show that the best performance is obtained by using audio-visual feedback with affordancemodulated IRL. The obtained results demonstrate the importance of multi-modal sensory processing integrated with goal-oriented knowledge in IRL tasks.

Matrix factorization is a key tool in data analysis; its applications include recommender systems, correlation analysis, signal processing, among others. Binary matrices are a particular case which has received significant attention for over thirty years, especially within the field of data mining. Dictionary learning refers to a family of methods for learning overcomplete basis (also called frames) in order to efficiently encode samples of a given type; this area, now also about twenty years old, was mostly developed within the signal processing field. In this work we propose two binary matrix factorization methods based on a binary adaptation of the dictionary learning paradigm to binary matrices. The proposed algorithms focus on speed and scalability; they work with binary factors combined with bit-wise operations and a few auxiliary integer ones. Furthermore, the methods are readily applicable to online binary matrix factorization. Another important issue in matrix factorization is the choice of rank for the factors; we address this model selection problem with an efficient method based on the Minimum Description Length principle. Our preliminary results show that the proposed methods are effective at producing interpretable factorizations of various data types of different nature.

Duplicates in data management are common and problematic. In this work, we present a translation of Datalog under bag semantics into a well-behaved extension of Datalog (the so-called warded Datalog+-) under set semantics. From a theoretical point of view, this allows us to reason on bag semantics by making use of the well-established theoretical foundations of set semantics. From a practical point of view, this allows us to handle the bag semantics of Datalog by powerful, existing query engines for the required extension of Datalog. Moreover, this translation has the potential for further extensions -- above all to capture the bag semantics of the semantic web query language SPARQL.

An AI that scrawls bikinis over nude photographs of women has been developed by scientists to block racy online images. The system, built at a Catholic institute in Brazil, automatically seeks out lewd pictures and digitally adds swimwear to speed up the process of censorsing images. Researchers warned that while the AI was designed to be used for good, cyber criminals could one day reverse the process to erase bikinis from people's photos. An AI that scrawls bikinis over nude photographs of women has been developed by scientists to block racy online images. Pictured are some of the AI's successful (centre row) and unsuccessful (bottom row) attempts to censor nude images (top line) The AI was trained by software engineers at the Pontifical Catholic University of Rio Grande do Sul using 2,000 images of women.

Germany Market Analysis Table 35: German Recent Past, Current & Future Analysis for Artificial Intelligence Analyzed with Annual Revenue Figures in US$ Million for Years 2015 through 2024 (includes corresponding Graph/Chart) 9.4.3 Italy Market Analysis Table 36: Italian Recent Past, Current & Future Analysis for Artificial Intelligence Analyzed with Annual Revenue Figures in US$ Million for Years 2015 through 2024 (includes corresponding Graph/Chart) 9.4.4

My name is Kris Coratti. Thank you for joining us on this very rainy morning. I'm glad you all made it out. We are going to have a fascinating series of discussion this morning on artificial intelligence. This is the latest in our ongoing event series that we call "Transformers." And our speakers this morning are going to explore the regulatory questions around this technology. They going to look at how AI is reshaping the way we live and work. And they're going to discuss how to make sure this technology is used responsibly in the future. Before we begin, I just want to quickly thank our presenting sponsor for this even, Software.org, And so now I'd like to go ahead and welcome to the stage The Washington Post's Tony Romm and Senators Maria Cantwell and Todd Young. And for those who don't know, Senator Cantwell is a Democrat from Washington State. Both are members of a Senate commerce committee which touches on artificial intelligence and many tech issues that we'll talk about today.

In recent years, with the popularization of deep learning frameworks and large datasets, researchers have started parallelizing their models in order to train faster. This is crucially important, because they typically explore many hyperparameters in order to find the best ones for their applications. This process is time consuming and, consequently, speeding up training improves productivity. One approach to parallelize deep learning models followed by many researchers is based on weak scaling. The minibatches increase in size as new GPUs are added to the system. In addition, new learning rates schedules have been proposed to fix optimization issues that occur with large minibatch sizes. In this paper, however, we show that the recommendations provided by recent work do not apply to models that lack large datasets. In fact, we argument in favor of using strong scaling for achieving reliable performance in such cases. We evaluated our approach with up to 32 GPUs and show that weak scaling not only does not have the same accuracy as the sequential model, it also fails to converge most of time. Meanwhile, strong scaling has good scalability while having exactly the same accuracy of a sequential implementation.